Najah Al-shanableh
@aabu.edu.jo
Computer Science
Al al-Bayt University
Experienced in teaching with a demonstrated history of working in the higher education industry. Skilled in ERP Implementations, Coaching, Software Engineering, Data Science, and Website Design. Strong education professional with an Interdisciplinary Doctorate focused in Healthcare Data Mining from New Mexico State University.
EDUCATION
Interdisciplinary Doctorate, Computer Science , NursingInterdisciplinary Doctorate, Computer Science , Nursing
New Mexico State University, 2011 - 2018
Master's degree, Computer ScienceMaster's degree, Computer Science
Al al-Bayt University, 2007 - 2010
Bachelor's degree, Software EngineeringBachelor's degree, Software Engineering
The Hashemite University, 2001 - 2005
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Science Applications, Artificial Intelligence, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Emad Tariq, Iman Akour, Najah Al-Shanableh, Enass Khalil Alquqa, Nidal Alzboun, Sulieman Ibraheem Shelash Al-Hawary, and Muhammad Turki Alshurideh
Growing Science
In this digital age, fraudulent practices are among the most challenging that organizations must be aware of due to the increasing use of online transactions. This also applies to the banking sector whose business has become more complex with the recent developments in information and communication technology, which has changed the nature of bank fraud requiring advanced prevention measures. From this perspective, this paper aims to determine how cybersecurity affects fraud prevention for Jordanian commercial banks. A five-dimensional NIST cybersecurity framework was used. The research data was collected from 173 information technology managers in commercial banks listed on the Amman Stock Exchange. Structural equation modeling (SEM) was applied to investigate research hypotheses. The results of the research demonstrated the significant impact of cybersecurity in fraud prevention, especially detect function which had the largest impact among the dimensions of cybersecurity. Therefore, a set of recommendations were formulated for policymakers in Jordanian commercial banks, the most important of which is the adoption of multi-factor authentication (MFA) approaches for customer accounts, employee access, and biometric systems that add an additional layer of protection and make access to sensitive information to unauthorized individuals more difficult.
Mazen Alzyoud, Raed Alazaidah, Mohammad Aljaidi, Ghassan Samara, Mais Haj Qasem, Muhammad Khalid, and Najah Al-Shanableh
Growing Science
Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.
Mazen Alzyoud, Najah Al-shanableh, Eman Nashnush, Rabah Shboul, Raed Alazaidah, Ghassan Samara, and Safaa Alhusban
International Association of Online Engineering (IAOE)
Ensuring the security of networks is a significant hurdle in the rollout of the Internet of Things (IoT). A widely used protocol in the IoT ecosystem is message queuing telemetry transport (MQTT), which is based on the published-subscribe model. IoT manufacturers are expected to expand their usage of the MQTT protocol, which is expected to increase the number of cyber security threats against the protocol. IoT settings are crucial to overcoming scalability and computing resource issues and minimizing the characteristics needed for categorization. Machine learning (ML) is extensively used in traffic categorization and intrusion detection. This study proposes a ML-based network traffic detection model (MLNTDM) to enhance IoT application layer attack detection. The proposed architecture for the MQTT protocol is evaluated based on its effectiveness in detecting malicious attacks and how these affect various MQTT brokers. This study focuses on low-power-consuming ML algorithms for detecting IoT botnet offenses and identifying typical attacks and their responses. With this framework, each network flow provides information that can help identify the source of generated traffic and network assaults. Results from our approach, as shown in the experiment, prove more accuracy.
Ghassan Samara, Abeer Al-Mohtaseb, Hayel Khafajeh, Raed Alazaidah, Omar Alidmat, Ahmad Nasayreh, Mazen Alzyoud, and Najah Al-shanableh
Growing Science
Cryptocurrencies are crucial in modern commerce and finance, whether at the national, corporate, or individual level. They serve as fundamental currencies for buying and selling, enabling various business transactions. However, the rise of cybercrime has brought about concerns regarding their operations, potential breaches in encrypted currencies, and the security systems managing them. The frequency of attack tactics and the motivation of attackers seeking financial gain are well-known. Many cryptocurrencies lack the necessary algorithms, techniques, and knowledge to effectively detect and mitigate malware, making them vulnerable targets for hackers. In this study, machine learning techniques are employed to detect malicious code in digital currencies. Additionally, a comparison of these techniques is conducted to determine the most suitable algorithm and technology, Furthermore, this study highlights the importance of effective malware detection in securing cryptocurrencies. Three datasets of different sizes were used, each yielding distinct results based on dataset size. The AdaBoost model demonstrated superior performance when applied to the short dataset, while the decision tree model performed best with the medium-sized dataset. Conversely, the Naive Bayes model consistently produced the worst results, while the large-size KNN model achieved the highest performance.
Mazen Alzyoud, Najah Al-Shanableh, Saleh Alomar, As’ad Mahmoud As’adAlnaser, Akram Mustafad, Ala’a Al-Momani, and Sulieman Ibraheem Shelash Al-Hawary
Growing Science
The growing significance of Artificial Intelligence (AI) across different fields highlights the essential role of user acceptance, as the success of this technology largely depends on its adoption and practical use by individuals. This research aims to examine how perceived cybersecurity, novelty value, and perceived trust affect students' willingness to accept AI in educational settings. The study's theoretical basis is the AI Device Use Acceptance (AIDUA) model. Using structural equation modeling, the study tested hypothesized relationships using data from 526 students at Jordanian universities. The results showed that social influence is positively associated with performance expectancy, while perceived cybersecurity is positively related to both performance and effort expectancy. Novelty value is positively associated with performance expectancy but a negative one with effort expectancy. Additionally, effort and performance expectancy significantly influence perceived trust and the willingness to accept AI. Moreover, perceived trust has a notable positive effect on the willingness to accept AI in education. These findings provide valuable guidance for the creation and improvement of AI-driven educational systems in universities, contributing to the broader understanding of AI technology acceptance in the educational field.
Najah Al-shanableh, Mazen Alzyoud, Saleh Alomar, Yousef Kilani, Eman Nashnush, Sulieman Al-Hawary, and Ala’a Al-Momani
Growing Science
While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights.
Mohammad Subhi Al-Batah, Enas Rezeg Al-Kwaldeh, Mutaz Abdel Wahed, Mazen Alzyoud, and Najah Al-Shanableh
Wiley
Image processing is a promising technique for enhancing images or extracting useful information from them. One commonly used density‐based clustering algorithm is DBSCAN (Density‐Based Spatial Clustering of Applications with Noise). However, DBSCAN struggles with satellite images due to their large sizes, often resulting in excessively long computation times. This research proposes an improved version of DBSCAN called “Enhanced DBSCAN‐based Histogram” (EDBSCAN‐H) to address these issues. EDBSCAN‐H enhances DBSCAN by incorporating a histogram‐based approach to better manage large datasets and reduce computation time. The key improvement lies in using the histogram of the input image and measuring the distance between data objects and histogram points to determine whether a region is dense or sparse, thereby selecting suitable parameters. EDBSCAN‐H introduces an additional parameter, ε2, alongside the original DBSCAN parameters ε₁ and MinPts. This enhancement allows EDBSCAN‐H to achieve improved performance across various metrics for clustering spatial data images.
Fatima Lahcen Yachou Aityassine, AbedElkareem Alzoubi, Ala’a M. Al-Momani, Najah Al-shanableh, Nancy S. Alajarmeh, Rehan Tareq Al-Majali, Mazen Alzyoud, Anber Abraheem Shlash Mohammad, Sulieman Ibraheem Shelash Al-Hawary, and Muhammad Turki Alshurideh
Springer Nature Switzerland
Ala’a M. Al-Momani, Mohammad Sarram, Saed Majed Zighan, Rehan Tareq Al-Majali, Najah Al-shanableh, Seyed Ghasem Saatchi, Tamather Majed Shatnawi, Nancy S. Alajarmeh, Sulieman Ibraheem Shelash Al-Hawary, and Anber Abraheem Shlash Mohammad
Springer Nature Switzerland
Najah Al-shanableh, Suhaib Anagreh, Ayman Ahmad Abu Haija, Mazen Alzyoud, Mohammad Azzam, Hussein Mousa Ahmad Maabreh, Nancy S. Alajarmeh, Mohammad Motasem Alrfai, Anber Abraheem Shlash Mohammad, and Sulieman Ibraheem Shelash Al-Hawary
Springer Nature Switzerland
Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Mazen Alzyoud, and Najah Al-Shanableh
Wiley
In today’s interconnected world, the transmission of both lengthy and concise text messages is ubiquitous across diverse communication platforms. With the proliferation of sensitive and specialized information being exchanged, safeguarding these messages from potential threats such as intruders, abusers, and data hackers becomes imperative. This study introduces an innovative approach aimed at streamlining data protection procedures while concurrently thwarting hacking attempts. At the core of this method lies the utilization of a sophisticated variable content private key designed to facilitate ease of alteration without compromising the integrity of encryption and decryption operations. The pivotal aspect involves leveraging a covert color image to generate this private key, mandating both the sender and receiver to securely retain this image. By employing a selected color image, the private key can seamlessly adapt to match the length of the confidential message. To fortify the level of security, the message is recommended to be segmented into blocks of predetermined sizes, with the sender and receiver jointly establishing these parameters. Subsequently, the bytes within each block are consolidated into a singular vector, which undergoes a left rotation by a predetermined number of bits as specified by the communicating parties. The proposed methodology is empirically validated through the implementation of various color images and text messages. Comparative analyses against existing methods underscore the efficacy and robustness of the proposed approach, substantiating its significant advancements in data protection paradigms.
Anber Abraheem Shlash Mohammad, Fatima Lahcen Yachou Aityassine, Zeid Naiel Aissa al-fugaha, Muhammad Turki Alshurideh, Nancy S. Alajarmeh, Ala’a Al-Momani, Najah Al-shanableh, Mazen Alzyoud, Sulieman Ibraheem Shelash Al-Hawary, and Abdullah Matar Al-Adamat
Springer Nature Switzerland
Nancy S. Alajarmeh, Fatima Lahcen Yachou Aityassine, Abed Elkareem Alzoubi, Zeid Naiel Aissa Al-Fugaha, Saed Majed Zighan, Mazen Alzyoud, Najah Al-Shanableh, Anber Abraheem Shlash Mohammad, Sulieman Ibraheem Shelash Al-Hawary, and Abdullah Matar Al-Adamat
Springer Nature Switzerland
Faisal Asad Farid Aburub, Razan Faisal Hamzeh, Mazen Alzyoud, Nancy S. Alajarmeh, Najah Al-shanableh, Rehan Tareq Al-Majali, Sulieman Ibraheem Shelash Al-Hawary, Muhammad Turki Alshurideh, and Faraj Mazyed Faraj Aldaihani
Springer Nature Switzerland
Tamather Majed Shatnawi, Muhammad Yassein Rahahle, Muhammad Turki Alshurideh, Muthnna Mohammad Khalaf Alkhawaldeh, Mazen Alzyoud, Najah Al-shanableh, Nancy S. Alajarmeh, Faraj Mazyed Faraj Aldaihani, Sulieman Ibraheem Shelash Al-Hawary, and Abdullah Ibrahim Mohammad
Springer Nature Switzerland
AbedElkareem Alzoubi, Mazen Alzyoud, Rehan Tareq Al-Majali, Najah Al-shanableh, Nancy S. Alajarmeh, Muthnna Mohammad Khalaf Alkhawaldeh, Ala’a Al-Momani, Fatima Lahcen Yachou Aityassine, Sulieman Ibraheem Shelash Al-Hawary, and Faraj Mazyed Faraj Aldaihani
Springer Nature Switzerland
Mazen Alzyoud, Nancy S. Alajarmeh, Tamather Majed Shatnawi, Anber Abraheem Shlash Mohammad, AbedElkareem Alzoubi, Zeid Naiel Aissa Al-fugaha, Ala’a Al-Momani, Najah Al-shanableh, Sulieman Ibraheem Shelash Al-Hawary, and Faraj Mazyed Faraj Aldaihani
Springer Nature Switzerland
Mohammad Sarram, Najah Al-shanableh, Suhaib Anagreh, Mohammad Motasem Alrfai, Muhammad Yassein Rahahle, Fatima Lahcen Yachou Aityassine, Seyed Ghasem Saatchi, Ayman Ahmad Abu Haija, Ala’a Al-Momani, and Sulieman Ibraheem Shelash Al-Hawary
Springer Nature Switzerland
Muhammad Turki Alshurideh, Tamather Majed Shatnawi, Ala’a Al-Momani, Anber Abraheem Shlash Mohammad, AbedElkareem Alzoubi, Mazen Alzyoud, Najah Al-shanableh, Nancy S. Alajarmeh, Sulieman Ibraheem Shelash Al-Hawary, and Faraj Mazyed Faraj Aldaihani
Springer Nature Switzerland
Faisal Asad Farid Aburub, Homam Abdulrazak-Ghazwan Al Rifai, Tariq Emad Arar, Muthnna Mohammad Khalaf Alkhawaldeh, Muhammad Turki Alshurideh, Sulieman Ibraheem Shelash Al-Hawary, Ala’a Al-Momani, Mazen Alzyoud, Nancy S. Alajarmeh, and Najah Al-shanableh
Springer Nature Switzerland
R. Alazaidah, A. Al-Shaikh, M. Al-Mousa, H. Khafajah, G. Samara, M. Alzyoud, N. Al-Shanableh and S. Almatarneh
Natural Sciences Publishing
: Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods.
Raed Alazaidah, Mazen Alzyoud, Najah Al-Shanableh, and Haneen Alzoubi
AIP Publishing
Haneen Alzoubi, Mazen Alzyoud, Raed Alazaidah, Najah Al-shanableh, Hayel Khafajah, and Sattam Almatarneh
IEEE
Multi-Label Classification (MLC) is a general type of classification that attracted scholars in the last few years. It imposes a high challenge since the problem search space of MLC is very large and follows an exponential function of growth. Moreover, the accuracy of classification in MLC is still very low when compared to the accuracy of Single Label Classification (SLC). Consequently, many scholars and researchers proposed to utilize and exploit the dependencies among class labels in to minimize the size of the problem search space of MLC, and hence, improve the accuracy of the classification task. Unfortunately, very few studies address this issue and attempt to discover the benefits of utilizing and exploiting the dependencies among labels in the domain of MLC. Therefore, this research attempts to identify the significance of discovering and utilizing these dependencies, with respect to three evaluation metrics designed specifically for MLC, and considering four different multi label datasets. The results revealed the clear significance of discovering and utilizing high order dependencies among labels in MLC, especially with high cardinality datasets.